Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Mike Wu

Second Advisor

Ted Hayduk

Third Advisor

Chris Pollett


Pose Estimatation, Action Recognition, Deep Learning


The emergence of large datasets and major improvements in Deep Learning has lead to many real-world applications. These applications have been focused on automotive markets, mobile markets, stock markets, and the healthcare market. Although Deep Learning has strong foundations across many areas, the few applications in Sports, Fitness, or even Injury Rehabilitation could benefit greatly from it. For example, if you are performing a workout and you need to evaluate your form, but do not have access or resources for an instructor to evaluate your form, it would be great to have an Artificial Intelligent agent provide real time feedback through your laptop or phone. Therefore our goal in this research study is to find a foundation for an exercise feedback application by comparing two computer vision models. The two approaches we will be comparing will be pose estimation and action recognition. The latter will be covered in more depth, as we will provide an end to end approach, while the former will be used as a benchmark to compare with. Action recognition will cover the collection, labeling, and organization of the data, training and integrating with real-time data to provide the user with feedback. The exercises we will focus on during our testing and analysis will be squats and push-ups. We were able to achieve an accuracy score of 79% with our best model, given a validation set of 391 squatting images from the PennAction dataset for squat exercise action recognition.